ISA-PID Controller Tuning: A combined min-max / ISE approach
|
|
- Shauna Manning
- 6 years ago
- Views:
Transcription
1 Proceedings of the 26 IEEE International Conference on Control Applications Munich, Germany, October 4-6, 26 FrB11.2 ISA-PID Controller Tuning: A combined min-max / ISE approach Ramon Vilanova, Pedro Balaguer Abstract This communication addresses the problem of tuning a PID controller on the basis of a Model Reference Specification and posterior inclusion of Robustness considerations. The tuning is based upon a First Order Plus Time Delay (FOPTD) model and aims to achieve a step response specification. The industrial ISA-PID formulation is chosen. First of all the expression for the structure of optimal controllers as providers of an approximation of such a reference model is got. A tuning rule is derived where the four parameters of the ISA-PID are determined by means of two new parameters: one parameter, T M, is related to the desired closed-loop time constant and the other one, z, that characterizes the approximation problem by means of the corresponding weighting function. As it is usual designs where a weighting function is used to set up the synthesis problem there should be some guide on how to select such weight. In this communication it is shown how this can be done by taking into account an ISE criterion. The introduction of ISE-like criterions for both parameters generates the optimal controller as a PI controller and the PID controller arises when detuning is introduced in order to increase the robustness. Index Terms PID Control, Robustness, FOPDT Models. I. INTRODUCTION Proportional-Integrative-Derivative (PID) controllers are with no doubt the most extensive option that can be found on industrial control applications. Their success is mainly due to its simple structure and meaning of the corresponding three parameters. This fact makes PID control easier to understand by the control engineer than other most advanced control techniques. Because of the widespread use of PID controllers it is interesting to have simple but efficient methods for tuning the controller. In fact, since Ziegler-Nichols proposed their first tuning rules [1], an intensive research has been done. From modifications of the original tuning rules [2], [3], [4] to a variety of new techniques: analytical tuning [5], [6]; optimization methods [7], [8]; gain and phase margin optimization [7], [9], just to mention a few. Recently, tuning methods based on optimization approaches with the aim of ensuring good stability robustness have received attention in the literature [1], [11]. However these methods, although effective, use to rely on somewhat complex optimization procedures and do not provide tuning rules. Instead, the tuning of the controller is defined as the solution of the optimization problem. The authors are with the Telecomunication and System Engineering Department, ETSE, Universitat Autonoma de Barcelona, 8193 Bellaterra, Barcelona, Spain. Corresponding author Ramon.Vilanova@uab.es Financial support from CICYT DPI is greatly appreciated The purpose of this paper is to obtain PID tuning rules based on a combination of a simple model description; First Order plus Time Delay (FOPTD); and closed loop specifications with robustness considerations. The tuning rules are given in two forms: firstly parameterized in terms of two parameters directly related to the desired reference model and approximation weighting function. To get the results as close as possible to the industrial situation, the widely used ISA structure [7] is chosen for the PID control law. The paper is organized as follows. Section 2 presents the problem formulation: process model, PID structure and the optimization problem based on a min-max formulation. Section 3 solves the min-max optimization problem and provides the controller structure and expression for the PID parameters. Starting from the optimal controller structure, Section 4 deals with the choice of the parameters that define the approximation problem in terms of Integral Square Error criteria. Simulation examples are presented in section 5 and final conclusions and considerations for further extensions are conducted in section 6. II. PROBLEM FORMULATION In this section the controller equations are presented as well as the assumed process model structure and the optimization problem that is posed in order to tune the PID controller. A. PID Controller There exists different ways to express the PID control law [12]. In this paper we concentrate on the ISA PID control law [7] [ u(s) = K p br(s) y(s)+ 1 (r(s) y(s)) (1) st i ] st d + (cr(s) y(s)) 1+sT d /N where r(s), y(s) and u(s) are the Laplace transforms of the reference, process output and control signal respectively. K p is the PID gain, T i and T d are the integral an derivative time constants, finally N is the ratio between T d and the time constant of an additional pole introduced to assure the properness of he controller. Parameters b and c are called set-point weights and constitute a simple way to obtain a 2- DOF controller. As their choice does not affect the feedback properties of the resulting controlled system, with no loss of /6/$2. 26 IEEE 2956
2 generality here we will assume b = c =1. This way, the PID transfer function we will work with can be written as 1+s(T i + T d N K(s) =K )+s2 T it d (N+1) N p st i (1 + s T d N ) (2) B. Process Model An important category of industrial processes can be represented by a First Order Plus Dead Time (FOPDT) model as. G n (s) = Ke Ls (3) 1+Ts where K is the process gain, T the time constant and L the time delay. This kind of models are easy to find by means of a simple step response experiment to get the process reaction curve. In order to deal with the delay term is usual to use a rational approximation. Here we will work with the following simple first order Taylor expansion of the e Ls term e Ls 1 Ls. (4) Obviously, this delay approximation has to be taken into account when analyzing the control system Robustness. As it will be seen in the provided example, the uncertainty is computed with respect to the e Ls delay term expression. C. Optimization problem In order to take into account robustness considerations, the design problem must be posed accordingly. One, rather usual, approach is to use frequency dependent uncertainty descriptions and to include them into the design problem [13]. Suppose the real process G(s) is given by the nominal model (3). An uncertainty description based on a multiplicative model error, Δ m (s) is defined as Δ m (s) =. G(s) G n(s) (5) G n (s) It is well known that a controller, K(s), that stabilizes the control system on the nominal system, also stabilizes all the control systems built up arround a family F of plants such that W m (s)t (s) < 1 (6) where T (s) is the nominal Complementary Sensitivity transfer function: T (s) = K(s)G n(s) (7) 1+G n (s)k(s) and W (s) is a frequency dependent weight that defines the family of plants: F = {G(s) =G n (s)(1 + Δ m (s)) : Δ m (jw) < W m (jw) } (8) However if one uses the Internal Model Control paradigm (IMC) that can be found in [13] it turns out that T (s) has a very simple expression; T (s) =G n (s)c(s); intermsof the so called IMC controller, or Youla parameter, for stable plants. The IMC synthesis gets C(s) on a first step and recover K(s) on a second step from: r r K(s) = K(s) C(s) 1 G n (s)c(s) d (a) d u C(s) (b) u G(s) G(s) G n (s) Fig. 1. Feedback Control System: (a) Conventional Configuration, (b) Internal Model Control configuration The main feature of the IMC method is that the desired closed loop time constant is provided as a tuning parameter, commonly known as the IMC filter. Robustness is dealt through the reduction of this desired closed loop bandwidth. A detailed description of the IMC synthesis method can be found in [13] [14]. Here we will make use of the IMC formulation just to set up the min-max problem we will base the design on. This way we will directly design the C(s) transfer function as the solution to the following problem y y (9) min W (s)(m(s) C(s)G n(s)) (1) C(s) where M(s) is a Reference Model for the closed loop system response and W (s) is a weighting function. In this communication we will use the Reference Model to specify the desired closed loop time constant, T M. Therefore it will take the form: M(s) = n M (s) d M (s) = 1 1+T M s (11) With respect to the weighting function, W (s), in order to automatically include integral action and keep it as simple as possible, we will assume the following form: W (s) = n W (s) d W (s) = γ 1+zs s (12) In order to include Robustness considerations, the solution to this minimization problem must be constrained to (6). 2957
3 III. SOLUTION TO THE MIN-MAX OPTIMIZATION PROBLEM This section will present a solution to the optimization problem (1). Several approaches exists to solve this H problem. See [15], [16] among others. Here we will follow a particularization of the solution presented in [17] where a polynomial approach was taken. This has the advantage of providing the structure of the optimal controller. Therefore, as we will do here, the problem statement can be constrained in order to provide a solution that leads to a PID controller. The problem at hand is, in fact, a min-max approximation problem : given two transfer functions M(s), N(s) RH find C(s) RH such that the following cost function in the -norm is minimized J = E(s) = W (s)(m(s) N(s)C(s)) (13) where N(s), M(s) and W (s) are factored as N(s) = n N (s) d N (s) M(s) = n M (s) d M (s) W (s) = n W (s) d W (s) The solution to the minimization of the cost function (13) lies in optimal interpolation theory [18]. First, factorize the plant numerator n N (s) as n N (s) = n + N (s)n N (s) where the polynomial n + N (s) only has stable roots and n N (s) is the remaining part. In order to obtain a unique factorisation the polynomial n + N (s) is assumed to be monic. Let ν =deg(n N (s)) and{z 1,z 2,..., z ν } be the distinct zeros of n N (s). From equation (13) it results that the error function E(s) is subjected to the following interpolation constraints: E(z i )=M(z i ) i =1...ν (14) If z i is a zero with multiplicity ν i, then additional differential interpolation constraints should be imposed. A well established theory [18], [19], [15] that solves this problem exists and a closed form solution can be obtained from the following lemma [15]: Lemma 3.1: The optimal E o (s) which minimizes E(s) is of an all-pass form E o (s) = { ρ q(s) q(s) if ν 1 if ν = (15) where q(s) =1+q 1 s + q 2 s q ν 1 s ν 1 is a strictly hurwitz polynomial and q (s) =q( s). Furthermore, the constants ρ and {q i } ν 1 i=1 are real and are uniquely determined by the interpolation constraints (14). Besides, the minimum cost of (13) is given by J o =min E(s) = E o (s) = ρ Now we will proceed with the application of this lemma in order to compute the optimal C(s) =C o (s). Note first that in our case ν =1and z 1 =1/L. Therefore the interpolation constraints give the following value for the optimal cost ρ: (L + z) ρ = W (1/L)M(1/L) = γl (L + T M ) (16) Application of the above lemma gives the following equation for the optimal parameter C o (s) then W (s)m(s) W (s)n(s)c o (s) =ρ q (s) q(s) ( ) C o (s) = (W (s)n(s)) 1 W (s)m(s) ρ q (s) q(s) d W (s)d N (s) = n W (s)n + N (s)n N (s) (17) ( nw (s)n M (s)q(s) ρq ) (s)d W (s)d M (s) d W (s)d M (s)q(s) In order for C o (s) to be a stable transfer function, n N (s) must be a factor of the numerator. That is to say, there must exist a polynomial χ(s) such that n N (s)χ(s) = n W (s)n M (s)q(s) (18) ρq (s)d W (s)d M (s) It follows that, to determine the optimal controller C o (s), the χ(s) polynomial must be known. In any case, the optimal C o (s) will obey to the following structure: C o (s) = d N (s)χ(s) n W (s)n + N (s)d M (s)q(s) (19) Moreover, as ν =1, it follows from the previous lemma that q(s) =q (s) =1.Also,d N (s) =(1+Ts), n + N (s) = K and d M (s) = (1 + T M (s)). Therefore, C o (s) further simplifies to C o (s) = 1 K (1 + Ts)χ(s) (1 + T M s)(1 + zs) (2) With respect to χ(s), it must obey to (18) so, if χ(s). = χ + χ 1 s, then: (1 Ls)(χ + χ 1 s) = (1+zs) (21) ρs γ (1 + T Ms) It is easily seen that χ =1 χ 1 = z + L ρ γ Therefore, the solution for the optimal C o (s) is C o (s) = 1 K p (1 + Ts)(1 + χ 1 s) (1 + T M s)(1 + zs) (22) (23) and the resulting optimal feedback K o (s) = C o (s)/(1 G n (s)c o (s)) becomes 2958
4 K o (s) = 1 K p (1 + Ts)(1 + χ 1 s) s(( ρ γ + T M)+T M ( ρ γ + z)s) = 1 (1 + Ts)(1 + χ 1 s) K p ( ρ γ + T (24) ( M) s(1 + T ρ γ +z) M ( ρ γ +TM )s) Thus, identifying (2) and (24) the following tuning relations arise K p = T i K(ρ/γ + T M ) T i = T + χ 1 T M (ρ/γ + z) (ρ/γ + T M ) T d = T M (ρ/γ + z) (ρ/γ + T M ) N (25) N = T T i ρ/γ L (ρ/γ + T M ) (ρ/γ + z) 1 Note that these relations provide all the PID parameters, including the derivative filter, N. The benefits of providing a tuning of this parameter have been reported in [2], [21]. Although the tuning relations (26) look somewhat complicated note they are directly expressed in terms of the process model (K, L and T ) and the definition of the optimization problem (γ, z and T M ). Moreover, note that γ always appear as ρ/γ. Therefore, because of (16) it results that ρ/γ is independent of γ. Without loss of generality we can assume γ =1and the previous relations simplify to: T i K p = K(ρ + T M ) (ρ + z) T i = T + χ 1 T M (ρ + T M ) (ρ + z) T d = T M (ρ + T M ) N (26) N = T T i ρ L (ρ + T M ) (ρ + z) 1 These tuning relations provide the four ISA-PID parameters parameterized in terms of the desired T M and z as determining the frequency range where the solution to (1) is to provide a better match. Next section gives z a meaning in terms of Robustness considerations. IV. z T M CONTROLLER TUNING Previous section provides, in addition of explicit tuning expressions for the K p, T i, T d and derivative filter constant N, the structure of the optimal IMC controller that leads to the ISA-PID controller. In fact, the tuning has been reformulate din terms of the z and T M parameters that define the approximation problem (1). The role of z will be to establish the frequency range where the approximation error is penalized. The choice of z will have a repercussion on the mismatch between the desired reference model and the achieved closed loop input-output relation. There are different ways of measuring this mismatch. Obviously one is the optimal value for J found above (16). However this is a worst case measure in the frequency domain. This is why we will look for the value of z that determines the approximation problem (1) in such a way that provides a minimum value for the following Integral Squared Error criterion: J ISE = [e(t)] 2 dt = [y M (t) y(t)] 2 dt (27) where y M (t) is the output of the reference model to an unit step change and y(t) the output provided by a controller that obeys to a structure given by: C o (s) = 1 (1 + Ts)χ(s) (28) K (1 + T M s)(1 + zs) If we compute E(s) =L[e(t)] = L[y M (t) y(t)] it turns out that 1 E(s) = 1+T M s (1 Ls)(1 + χ1 s) (1 + T M s)(1 + zs) = ρ 1+zs therefore the time domain solution is obtained as: (29) e(t) = ρ z e t/z (3) By introducing (3) into (27) results in: J ISE = ρ2 (31) 2z Taking the derivative of (31) with respect to z and equating to zero produces z = L. Therefore independent of T M.Asan example, figure (2) shows evaluations of the ISE functional (31) with respect to z for different values of T M and L =1. ISE ISE cost function wrt z for different values of TM z Fig. 2. J ISE functional (31) for different values of T M and L =1. In terms of the original optimization problem, the value of z that gives the lowest ρ is z =(z ). Note however that this will cause very low penalty ( ) fortheerrorat mid-high frequencies. Therefore not very realistic. This is 2959
5 the reason the value z = L can be taken as an answer to the question of how to design the weighting function. Therefore, for z L we are relaxing the matching - according to the J ISE measure - that the controller achieves. To select a value z<lor L<zcan be considered as a detuning process. What are the repercussions of such detuning? Will look at the effect that this parameter has on the achieved robustness margin. Assuming we have the set of plants defined in terms of an uncertainty description weight W m (s), the Robust Stability constraint takes the form: G n (s)c(s)w m (s) < 1 K 1 Ls 1 (1 + Ts)(1 + χ 1 s) p 1+TsK p (1 + T M s)(1 + zs) W m(s) < 1 (1 Ls)(1 + χ 1 s) (1 + T M s)(1 + zs) W m(s) < 1 (32) (1 Ljw)(1 + χ 1 jw) (1 + T M jw)(1 + zjw) < 1 W m (jw) w Now, once T M is fixed (as L is given by the process model), and recalling from (22) that χ 1 is also depending of z, the choice of z will obey to the satisfaction of a constraint of the form: (1 + χ 1 jw) 1+zjw < (1 + T M jw) 1 (1 Ljw) W m (jw) w (33) As 1/W m (s) usually has a low pass form, in order to increase the robustness of the design it would be desired that χ 1 <z. By (22) and (16) this is implied by z>t M. At this point, in order to complete the design, just the selection of T M is left to be done. This can be left as a free tuning parameter and obtain the ISA-PID parameters from (26) once it is fixed from the designer. However it seems necessary to introduce another criterion to help in the selection of T M. Up to now we have been concerned with reference step change performance. However, the choice of the desired closed-loop constant will have direct repercussion on the load-disturbance attenuation performance. Therefore it seems reasonable to introduce this kind of considerations when selecting T M. The load disturbance performance can be expressed in terms of the integrated absolute error due to a load disturbance in the form of a unit step at the process input: J IAE = e(t) dt This criterion is difficult to deal with analytically. On the other hand, the integrated error criterion J IE = e(t)dt is much more convenient. In [7] it is shown that IE = T i. Thus, the criterion IE is directly given by the integrating gain of the controller. Both criterion will be identical IE = IAE if the error is positive. Furthermore, if the system is well damped the criteria will be close. First of all we will concentrate on the situation where z = L as suggested above. Since in our case T i = T + χ 1 (ρ+z) T M (ρ+t M ),ifwesolvefor T i T M = z=l it is found that T M = L. If we put these values into (26) we obtain that T i = T, K p = T/2KL, T d = and N =. Therefore, the controller arising from a minimization problem of the form (1), defined by z and T M taking values that minimizes J ISE and J IE, respectively, is a PI controller. Moreover, this controller results identical to an IMC tuned PI controller according to [13] [22] with a closed loop constant equal to L. From this fact we give the detuning procedure we were introducing above as to change from a PI to a PID controller. Therefore, the introduction of robustness considerations makes us to change from a PI to a PID controller. Note with the choice T M = L, constraint (33) becomes (1 + χ 1 jw) 1+zjw < 1 W m (jw) w (34) V. SIMULATION EXAMPLE This section presents a simulation example that shows the application of the outlined design method. Even the example presented here is purely academical, a more complete application has been developed and can be found in [23]. The purpose is to show how, the introduction of z as a robustness parameter - detuned with respect to z = L - provides better performance over all the family set built up around the nominal design. As it has been stated, the design is based upon a FOPTD nominal process model where the time delay has been approximated by using (4). Therefore, consider the following nominal model: 1 L n s G n (s) =K n 1+T n s with K n = L n = T n =1. In addition, an uncertainty of 3% associated to each parameter is considered. The We will compare the tuning arising from the choice T M = L n and z = L n (therefore a PI controller) with that resulting from increasing z such that (32) is satisfied. In order to guide the selection of z the inverse of the multiplicative uncertainty, Δ m (jw), of the members of the plant family 1 is plotted against C(jw)G n (jw). In order to find the value of z that allows for Robust Stability the initial value of z = L =1is increased and it is found that the value of z that satisfies the robust stability constraint is z 1.4. This way, the step and load disturbance responses of the PI controller and PID controller with respect 1 It is worth to notice that the uncertainty has been computed with respect to G(s) =K e Ls. This way the effect of the delay approximation (4) is 1+Ts also taken into account. 296
6 Sensitivity constraint in order to better improve disturbance attenuation. This way a mixed sensitivity problem will need to be solved. Although optimization approaches based on non-convex numerical methods could be used it would be helpful if an analytical solution along the lines of the one presented could be found. Also, considerations to include the set-point weights and to design the overall ISA-PID controller are being considered. VII. ACKNOWLEDGMENTS This work is supported by the Spanish CICYT program under contract DPI Fig. 3. Robust Stability Constraint to the plant family is shown in figure (4). It can be seen that the response of the PID controller over all the family set is closer to the nominal one that of the PI controller. Fig. 4. family Step responses of the ISA-PI and ISA-PID with respect to the plant VI. CONCLUSIONS Tuning relations for PID design have been presented. In order to get results closer to Industrial applications the discussion has concentrated on the ISA formulation. The design has been done from a min-max optimization problem stated in terms of a desired time constant for the closed-loop step response, T M. The approximation problem is also defined in terms of a weighting function characterized by a parameter z. The closed-form of the solution to the minimization problem has allowed to get an optimal way to define the problem. This is to say how to choose T M and z. These optimal values turned the controller into a PI. Starting from this PI, if more robustness is needed, deviations from the optimal situation, T M = z = L, will detune the PI controller and generate a PID controller with better robustness margins. Future work is conducted to introduce a REFERENCES [1] J. Ziegler and N. Nichols, Optimum settings for automatic controllers, Trans. ASME, pp , [2] I. Chien, J. Hrones, and J. Reswick, On the automatic control of generalized passive systems, Trans. ASME, pp , [3] C. Hang, K. Astrom, and W. Ho, Refinement of the ziegler nichols formula, IEE Proceedings.Part D., vol. 138, pp , [4] K. Astrom and T. Hgglund, Revisiting the ziegler nichols step response method for pid control, Journal of Process Control, vol. 14, pp , 24. [5] S. Hwang and H. Chang, Theoretical examination of closed-loop properties and tuning methods of single loop pi controllers, Chem. Eng. Sci, vol. 42, pp , [6] S. Skogestad, Simple analytic rules for model reduction and pid controller tuning, Journal of Process Control, vol. 13, pp , 23. [7] K. Astrom and T. Hgglund, PID Controller: Theory, Design and Tuning. Instrument of Society of America, [8] K. Astrom, H. Panagopoulos, and T. Hgglund, Design of pi controllers based on non-convex optimization, Automatica, vol. 34, pp , [9] H. Fung, Q. Wang, and T. Lee, Pi tuning in terms of gain and phase margins, Automatica, vol. 34, pp , [1] M. Ge, M. Chiu, and Q. Wang, Robust pid controller design via lmi approach, Journal of Process Control, vol. 12, pp. 3 13, 22. [11] R. Toscano, A simple pi/pid controller design method via numerical optimizatio approach, Journal of Process Control, vol. 15, pp , 25. [12] P. Cominos and N. Munro, Pid controllers: recent tuning methods and design to specification, IEE Proceedings.Part D., vol. 149, pp , 22. [13] M. Morari and E. Zafirou, Robust Process Control. Englewood Cliffs, NJ, Prentice-Hall, [14] Y. Lee, S. Park, M. Lee, and C. Brosilow, Pid controller tuning for desired closed-loop responses for si/so systems, AIChe J., vol. 44, pp , [15] B. Chen, Controller synthesis of optimal sensitivity: multivariable case, IEE Proceedings.Part D., vol. 131, pp , [16] B. Francis, A Course in H Control theory. Springer Verlag, [17] R. Vilanova and I. Serra, Model reference control in two degree of freedom control systems: adaptive min-max approach, IEE Proceedings.Part D., vol. 146, pp , [18] D. Sarason, Generalized interpolation in, Trans. AMS, vol.127, pp , [19] G. Zames and B. Francis, Feedback, minmax sensitivity and optimal robustness, IEEE Trans. Automat. Contr., vol. 28, pp , [2] A. Isaakson and S. Graebe, Derivative filter is an integral part of pid design, IEE Proceedings.Part D., vol. 149, pp , 22. [21] W. Luyben, Effect of derivative algorithm and tuning selection on the pid control of dead-time processes, Industrial Engineering Chemistry Research, vol. 4, pp , 21. [22] D. E. Rivera, M. Morari, and S. Skogestad, Internal model control 4. pid controller design, Ind. Eng. Chem. Res., vol. 25, pp , [23] R. Vilanova, Three-term controller design with sensitivity considerations: application to a continuous stirred tank reactor, Submitted to UKACC, International Conference on Control,
Model-based PID tuning for high-order processes: when to approximate
Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 25 Seville, Spain, December 2-5, 25 ThB5. Model-based PID tuning for high-order processes: when to approximate
More informationIan G. Horn, Jeffery R. Arulandu, Christopher J. Gombas, Jeremy G. VanAntwerp, and Richard D. Braatz*
Ind. Eng. Chem. Res. 996, 35, 3437-344 3437 PROCESS DESIGN AND CONTROL Improved Filter Design in Internal Model Control Ian G. Horn, Jeffery R. Arulandu, Christopher J. Gombas, Jeremy G. VanAntwerp, and
More informationA NEW APPROACH TO MIXED H 2 /H OPTIMAL PI/PID CONTROLLER DESIGN
Copyright 2002 IFAC 15th Triennial World Congress, Barcelona, Spain A NEW APPROACH TO MIXED H 2 /H OPTIMAL PI/PID CONTROLLER DESIGN Chyi Hwang,1 Chun-Yen Hsiao Department of Chemical Engineering National
More informationIMC based automatic tuning method for PID controllers in a Smith predictor configuration
Computers and Chemical Engineering 28 (2004) 281 290 IMC based automatic tuning method for PID controllers in a Smith predictor configuration Ibrahim Kaya Department of Electrical and Electronics Engineering,
More informationA Method for PID Controller Tuning Using Nonlinear Control Techniques*
A Method for PID Controller Tuning Using Nonlinear Control Techniques* Prashant Mhaskar, Nael H. El-Farra and Panagiotis D. Christofides Department of Chemical Engineering University of California, Los
More informationFeedback Control of Linear SISO systems. Process Dynamics and Control
Feedback Control of Linear SISO systems Process Dynamics and Control 1 Open-Loop Process The study of dynamics was limited to open-loop systems Observe process behavior as a result of specific input signals
More informationMULTILOOP PI CONTROLLER FOR ACHIEVING SIMULTANEOUS TIME AND FREQUENCY DOMAIN SPECIFICATIONS
Journal of Engineering Science and Technology Vol. 1, No. 8 (215) 113-1115 School of Engineering, Taylor s University MULTILOOP PI CONTROLLER FOR ACHIEVING SIMULTANEOUS TIME AND FREQUENCY DOMAIN SPECIFICATIONS
More informationCHAPTER 3 TUNING METHODS OF CONTROLLER
57 CHAPTER 3 TUNING METHODS OF CONTROLLER 3.1 INTRODUCTION This chapter deals with a simple method of designing PI and PID controllers for first order plus time delay with integrator systems (FOPTDI).
More informationCompensatorTuning for Didturbance Rejection Associated with Delayed Double Integrating Processes, Part II: Feedback Lag-lead First-order Compensator
CompensatorTuning for Didturbance Rejection Associated with Delayed Double Integrating Processes, Part II: Feedback Lag-lead First-order Compensator Galal Ali Hassaan Department of Mechanical Design &
More informationRobust Fractional Control An overview of the research activity carried on at the University of Brescia
Università degli Studi di Brescia Dipartimento di Ingegneria dell Informazione Robust Fractional Control An overview of the research activity carried on at the University of Brescia Fabrizio Padula and
More informationPID control of FOPDT plants with dominant dead time based on the modulus optimum criterion
Archives of Control Sciences Volume 6LXII, 016 No. 1, pages 5 17 PID control of FOPDT plants with dominant dead time based on the modulus optimum criterion JAN CVEJN The modulus optimum MO criterion can
More informationControl of integral processes with dead time Part IV: various issues about PI controllers
Control of integral processes with dead time Part IV: various issues about PI controllers B. Wang, D. Rees and Q.-C. Zhong Abstract: Various issues about integral processes with dead time controlled by
More informationDesign and Tuning of Fractional-order PID Controllers for Time-delayed Processes
Design and Tuning of Fractional-order PID Controllers for Time-delayed Processes Emmanuel Edet Technology and Innovation Centre University of Strathclyde 99 George Street Glasgow, United Kingdom emmanuel.edet@strath.ac.uk
More information2-DoF Decoupling controller formulation for set-point following on Decentraliced PI/PID MIMO Systems
2-DoF Decoupling controller formulation for set-point following on Decentraliced PI/PID MIMO Systems R. Vilanova R. Katebi Departament de Telecomunicació i d Enginyeria de Sistemes, Escola d Enginyeria,
More informationImproved cascade control structure for enhanced performance
Improved cascade control structure for enhanced performance Article (Unspecified) Kaya, İbrahim, Tan, Nusret and Atherton, Derek P. (7) Improved cascade control structure for enhanced performance. Journal
More informationAn Improved Relay Auto Tuning of PID Controllers for SOPTD Systems
Proceedings of the World Congress on Engineering and Computer Science 7 WCECS 7, October 4-6, 7, San Francisco, USA An Improved Relay Auto Tuning of PID Controllers for SOPTD Systems Sathe Vivek and M.
More informationA unified approach for proportional-integral-derivative controller design for time delay processes
Korean J. Chem. Eng., 32(4), 583-596 (2015) DOI: 10.1007/s11814-014-0237-6 INVITED REVIEW PAPER INVITED REVIEW PAPER pissn: 0256-1115 eissn: 1975-7220 A unified approach for proportional-integral-derivative
More informationMULTILOOP CONTROL APPLIED TO INTEGRATOR MIMO. PROCESSES. A Preliminary Study
MULTILOOP CONTROL APPLIED TO INTEGRATOR MIMO PROCESSES. A Preliminary Study Eduardo J. Adam 1,2*, Carlos J. Valsecchi 2 1 Instituto de Desarrollo Tecnológico para la Industria Química (INTEC) (Universidad
More informationMRAGPC Control of MIMO Processes with Input Constraints and Disturbance
Proceedings of the World Congress on Engineering and Computer Science 9 Vol II WCECS 9, October -, 9, San Francisco, USA MRAGPC Control of MIMO Processes with Input Constraints and Disturbance A. S. Osunleke,
More informationTHE ANNALS OF "DUNAREA DE JOS" UNIVERSITY OF GALATI FASCICLE III, 2000 ISSN X ELECTROTECHNICS, ELECTRONICS, AUTOMATIC CONTROL, INFORMATICS
ELECTROTECHNICS, ELECTRONICS, AUTOMATIC CONTROL, INFORMATICS ON A TAKAGI-SUGENO FUZZY CONTROLLER WITH NON-HOMOGENOUS DYNAMICS Radu-Emil PRECUP and Stefan PREITL Politehnica University of Timisoara, Department
More informationCHBE320 LECTURE XI CONTROLLER DESIGN AND PID CONTOLLER TUNING. Professor Dae Ryook Yang
CHBE320 LECTURE XI CONTROLLER DESIGN AND PID CONTOLLER TUNING Professor Dae Ryook Yang Spring 2018 Dept. of Chemical and Biological Engineering 11-1 Road Map of the Lecture XI Controller Design and PID
More informationA Design Method for Smith Predictors for Minimum-Phase Time-Delay Plants
00 ECTI TRANSACTIONS ON COMPUTER AND INFORMATION TECHNOLOGY VOL., NO.2 NOVEMBER 2005 A Design Method for Smith Predictors for Minimum-Phase Time-Delay Plants Kou Yamada Nobuaki Matsushima, Non-members
More informationThe parameterization of all. of all two-degree-of-freedom strongly stabilizing controllers
The parameterization stabilizing controllers 89 The parameterization of all two-degree-of-freedom strongly stabilizing controllers Tatsuya Hoshikawa, Kou Yamada 2, Yuko Tatsumi 3, Non-members ABSTRACT
More informationA Note on Bode Plot Asymptotes based on Transfer Function Coefficients
ICCAS5 June -5, KINTEX, Gyeonggi-Do, Korea A Note on Bode Plot Asymptotes based on Transfer Function Coefficients Young Chol Kim, Kwan Ho Lee and Young Tae Woo School of Electrical & Computer Eng., Chungbuk
More informationInternal Model Control of A Class of Continuous Linear Underactuated Systems
Internal Model Control of A Class of Continuous Linear Underactuated Systems Asma Mezzi Tunis El Manar University, Automatic Control Research Laboratory, LA.R.A, National Engineering School of Tunis (ENIT),
More informationRobust Performance Example #1
Robust Performance Example # The transfer function for a nominal system (plant) is given, along with the transfer function for one extreme system. These two transfer functions define a family of plants
More informationIterative Controller Tuning Using Bode s Integrals
Iterative Controller Tuning Using Bode s Integrals A. Karimi, D. Garcia and R. Longchamp Laboratoire d automatique, École Polytechnique Fédérale de Lausanne (EPFL), 05 Lausanne, Switzerland. email: alireza.karimi@epfl.ch
More informationResearch Article Volume 6 Issue No. 6
DOI 1.41/216.1797 ISSN 2321 3361 216 IJESC ` Research Article Volume 6 Issue No. 6 Design of Multi Loop PI/PID Controller for Interacting Multivariable Process with Effective Open Loop Transfer Function
More informationResearch Article. World Journal of Engineering Research and Technology WJERT.
wjert, 2015, Vol. 1, Issue 1, 27-36 Research Article ISSN 2454-695X WJERT www.wjert.org COMPENSATOR TUNING FOR DISTURBANCE REJECTION ASSOCIATED WITH DELAYED DOUBLE INTEGRATING PROCESSES, PART I: FEEDBACK
More informationFEEDFORWARD CONTROLLER DESIGN BASED ON H ANALYSIS
271 FEEDFORWARD CONTROLLER DESIGN BASED ON H ANALYSIS Eduardo J. Adam * and Jacinto L. Marchetti Instituto de Desarrollo Tecnológico para la Industria Química (Universidad Nacional del Litoral - CONICET)
More informationControl Systems Design
ELEC4410 Control Systems Design Lecture 3, Part 2: Introduction to Affine Parametrisation School of Electrical Engineering and Computer Science Lecture 3, Part 2: Affine Parametrisation p. 1/29 Outline
More informationRobust Loop Shaping Controller Design for Spectral Models by Quadratic Programming
Robust Loop Shaping Controller Design for Spectral Models by Quadratic Programming Gorka Galdos, Alireza Karimi and Roland Longchamp Abstract A quadratic programming approach is proposed to tune fixed-order
More informationComputation of Stabilizing PI and PID parameters for multivariable system with time delays
Computation of Stabilizing PI and PID parameters for multivariable system with time delays Nour El Houda Mansour, Sami Hafsi, Kaouther Laabidi Laboratoire d Analyse, Conception et Commande des Systèmes
More informationMultivariable decoupling set-point approach applied to a wastewater treatment
02043 201) DOI: 10.1051/ matecconf/201702043 Multivariable decoupling set-point approach applied to a wastewater treatment plant Ramon Vilanova 1,,a and Orlando Arrieta 2, 1 Departament de Telecomunicació
More informationA Tuning of the Nonlinear PI Controller and Its Experimental Application
Korean J. Chem. Eng., 18(4), 451-455 (2001) A Tuning of the Nonlinear PI Controller and Its Experimental Application Doe Gyoon Koo*, Jietae Lee*, Dong Kwon Lee**, Chonghun Han**, Lyu Sung Gyu, Jae Hak
More informationComparative study of three practical IMC algorithms with inner controller of first and second order
Journal of Electrical Engineering, Electronics, Control and Computer Science JEEECCS, Volume 2, Issue 4, pages 2-28, 206 Comparative study of three practical IMC algorithms with inner controller of first
More informationA DESIGN METHOD FOR SIMPLE REPETITIVE CONTROLLERS WITH SPECIFIED INPUT-OUTPUT CHARACTERISTIC
International Journal of Innovative Computing, Information Control ICIC International c 202 ISSN 349-498 Volume 8, Number 7(A), July 202 pp. 4883 4899 A DESIGN METHOD FOR SIMPLE REPETITIVE CONTROLLERS
More information2 Problem formulation. Fig. 1 Unity-feedback system. where A(s) and B(s) are coprime polynomials. The reference input is.
Synthesis of pole-zero assignment control law with minimum control input M.-H. TU C.-M. Lin Indexiny ferms: Control systems, Pules and zeros. Internal stability Abstract: A new method of control system
More informationA brief introduction to robust H control
A brief introduction to robust H control Jean-Marc Biannic System Control and Flight Dynamics Department ONERA, Toulouse. http://www.onera.fr/staff/jean-marc-biannic/ http://jm.biannic.free.fr/ European
More informationController Parameters Dependence on Model Information Through Dimensional Analysis
Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference Shanghai, PR China, December 16-18, 2009 WeBIn49 Controller Parameters Dependence on Model Information hrough Dimensional
More informationProcess Identification for an SOPDT Model Using Rectangular Pulse Input
Korean J. Chem. Eng., 18(5), 586-592 (2001) SHORT COMMUNICATION Process Identification for an SOPDT Model Using Rectangular Pulse Input Don Jang, Young Han Kim* and Kyu Suk Hwang Dept. of Chem. Eng., Pusan
More informationImproved Autotuning Using the Shape Factor from Relay Feedback
Article Subscriber access provided by NATIONAL TAIWAN UNIV Improved Autotuning Using the Shape Factor from Relay Feedback T. Thyagarajan, and Cheng-Ching Yu Ind. Eng. Chem. Res., 2003, 42 (20), 4425-4440
More informationImproving PID Controller Disturbance Rejection by Means of Magnitude Optimum
J. Stefan Institute, Ljubljana, Slovenia Report DP-8955 Improving Controller Disturbance Rejection by Means of Magnitude Optimum Damir Vrančić and Satja Lumbar * * Faculty of Electrical Engineering, University
More informationA unified double-loop multi-scale control strategy for NMP integrating-unstable systems
Home Search Collections Journals About Contact us My IOPscience A unified double-loop multi-scale control strategy for NMP integrating-unstable systems This content has been downloaded from IOPscience.
More informationH-Infinity Controller Design for a Continuous Stirred Tank Reactor
International Journal of Electronic and Electrical Engineering. ISSN 974-2174 Volume 7, Number 8 (214), pp. 767-772 International Research Publication House http://www.irphouse.com H-Infinity Controller
More informationShould we forget the Smith Predictor?
FrBT3. Should we forget the Smith Predictor? Chriss Grimholt Sigurd Skogestad* Abstract: The / controller is the most used controller in industry. However, for processes with large time delays, the common
More informationIterative Feedback Tuning for robust controller design and optimization
Iterative Feedback Tuning for robust controller design and optimization Hynek Procházka, Michel Gevers, Brian D.O. Anderson, Christel Ferrera Abstract This paper introduces a new approach for robust controller
More informationRobust and Optimal Control, Spring A: SISO Feedback Control A.1 Internal Stability and Youla Parameterization
Robust and Optimal Control, Spring 2015 Instructor: Prof. Masayuki Fujita (S5-303B) A: SISO Feedback Control A.1 Internal Stability and Youla Parameterization A.2 Sensitivity and Feedback Performance A.3
More informationA FEEDBACK STRUCTURE WITH HIGHER ORDER DERIVATIVES IN REGULATOR. Ryszard Gessing
A FEEDBACK STRUCTURE WITH HIGHER ORDER DERIVATIVES IN REGULATOR Ryszard Gessing Politechnika Śl aska Instytut Automatyki, ul. Akademicka 16, 44-101 Gliwice, Poland, fax: +4832 372127, email: gessing@ia.gliwice.edu.pl
More informationAvailable online at ScienceDirect. Procedia Engineering 100 (2015 )
Available online at www.sciencedirect.com ScienceDirect Procedia Engineering 100 (015 ) 345 349 5th DAAAM International Symposium on Intelligent Manufacturing and Automation, DAAAM 014 Control of Airflow
More informationRobust QFT-based PI controller for a feedforward control scheme
Integral-Derivative Control, Ghent, Belgium, May 9-11, 218 ThAT4.4 Robust QFT-based PI controller for a feedforward control scheme Ángeles Hoyo José Carlos Moreno José Luis Guzmán Tore Hägglund Dep. of
More informationDECENTRALIZED PI CONTROLLER DESIGN FOR NON LINEAR MULTIVARIABLE SYSTEMS BASED ON IDEAL DECOUPLER
th June 4. Vol. 64 No. 5-4 JATIT & LLS. All rights reserved. ISSN: 99-8645 www.jatit.org E-ISSN: 87-395 DECENTRALIZED PI CONTROLLER DESIGN FOR NON LINEAR MULTIVARIABLE SYSTEMS BASED ON IDEAL DECOUPLER
More informationRobust PID and Fractional PI Controllers Tuning for General Plant Model
2 مجلة البصرة للعلوم الهندسية-المجلد 5 العدد 25 Robust PID and Fractional PI Controllers Tuning for General Plant Model Dr. Basil H. Jasim. Department of electrical Engineering University of Basrah College
More informationLow-order feedback-feedforward controller for dead-time processes with measurable disturbances
Preprint, 11th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems Low-order feedback-feedforward controller for dead-time processes with measurable disturbances Carlos Rodríguez
More informationDesign and Implementation of Sliding Mode Controller using Coefficient Diagram Method for a nonlinear process
IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-issn: 2278-1676,p-ISSN: 2320-3331, Volume 7, Issue 5 (Sep. - Oct. 2013), PP 19-24 Design and Implementation of Sliding Mode Controller
More informationChapter 2. Classical Control System Design. Dutch Institute of Systems and Control
Chapter 2 Classical Control System Design Overview Ch. 2. 2. Classical control system design Introduction Introduction Steady-state Steady-state errors errors Type Type k k systems systems Integral Integral
More informationLet the plant and controller be described as:-
Summary of Fundamental Limitations in Feedback Design (LTI SISO Systems) From Chapter 6 of A FIRST GRADUATE COURSE IN FEEDBACK CONTROL By J. S. Freudenberg (Winter 2008) Prepared by: Hammad Munawar (Institute
More informationAutomatic Control 2. Loop shaping. Prof. Alberto Bemporad. University of Trento. Academic year
Automatic Control 2 Loop shaping Prof. Alberto Bemporad University of Trento Academic year 21-211 Prof. Alberto Bemporad (University of Trento) Automatic Control 2 Academic year 21-211 1 / 39 Feedback
More informationRobust fixed-order H Controller Design for Spectral Models by Convex Optimization
Robust fixed-order H Controller Design for Spectral Models by Convex Optimization Alireza Karimi, Gorka Galdos and Roland Longchamp Abstract A new approach for robust fixed-order H controller design by
More informationRobust multivariable pid design via iterative lmi
Robust multivariable pid design via iterative lmi ERNESTO GRANADO, WILLIAM COLMENARES, OMAR PÉREZ Universidad Simón Bolívar, Departamento de Procesos y Sistemas. Caracas, Venezuela. e- mail: granado, williamc,
More informationCHAPTER 6 CLOSED LOOP STUDIES
180 CHAPTER 6 CLOSED LOOP STUDIES Improvement of closed-loop performance needs proper tuning of controller parameters that requires process model structure and the estimation of respective parameters which
More informationParameter Estimation of Single and Decentralized Control Systems Using Pulse Response Data
Parameter Estimation of Single and Decentralized Control Systems Bull. Korean Chem. Soc. 003, Vol. 4, No. 3 79 Parameter Estimation of Single and Decentralized Control Systems Using Pulse Response Data
More informationTHE PARAMETERIZATION OF ALL ROBUST STABILIZING MULTI-PERIOD REPETITIVE CONTROLLERS FOR MIMO TD PLANTS WITH THE SPECIFIED INPUT-OUTPUT CHARACTERISTIC
International Journal of Innovative Computing, Information Control ICIC International c 218 ISSN 1349-4198 Volume 14, Number 2, April 218 pp. 387 43 THE PARAMETERIZATION OF ALL ROBUST STABILIZING MULTI-PERIOD
More informationIdentification for Control with Application to Ill-Conditioned Systems. Jari Böling
Identification for Control with Application to Ill-Conditioned Systems Jari Böling Process Control Laboratory Faculty of Chemical Engineering Åbo Akademi University Åbo 2001 2 ISBN 952-12-0855-4 Painotalo
More informationHANKEL-NORM BASED INTERACTION MEASURE FOR INPUT-OUTPUT PAIRING
Copyright 2002 IFAC 15th Triennial World Congress, Barcelona, Spain HANKEL-NORM BASED INTERACTION MEASURE FOR INPUT-OUTPUT PAIRING Björn Wittenmark Department of Automatic Control Lund Institute of Technology
More informationSimulation based Modeling and Implementation of Adaptive Control Technique for Non Linear Process Tank
Simulation based Modeling and Implementation of Adaptive Control Technique for Non Linear Process Tank P.Aravind PG Scholar, Department of Control and Instrumentation Engineering, JJ College of Engineering
More informationA Simple PID Control Design for Systems with Time Delay
Industrial Electrical Engineering and Automation CODEN:LUTEDX/(TEIE-7266)/1-16/(2017) A Simple PID Control Design for Systems with Time Delay Mats Lilja Division of Industrial Electrical Engineering and
More informationLinear State Feedback Controller Design
Assignment For EE5101 - Linear Systems Sem I AY2010/2011 Linear State Feedback Controller Design Phang Swee King A0033585A Email: king@nus.edu.sg NGS/ECE Dept. Faculty of Engineering National University
More informationA design method for two-degree-of-freedom multi-period repetitive controllers for multiple-input/multiple-output systems
A design method for two-degree-of-freedom multi-period repetitive controllers for multiple-input/multiple-output systems Zhongxiang Chen Kou Yamada Tatsuya Sakanushi Iwanori Murakami Yoshinori Ando Nhan
More informationFEL3210 Multivariable Feedback Control
FEL3210 Multivariable Feedback Control Lecture 5: Uncertainty and Robustness in SISO Systems [Ch.7-(8)] Elling W. Jacobsen, Automatic Control Lab, KTH Lecture 5:Uncertainty and Robustness () FEL3210 MIMO
More informationQUANTITATIVE L P STABILITY ANALYSIS OF A CLASS OF LINEAR TIME-VARYING FEEDBACK SYSTEMS
Int. J. Appl. Math. Comput. Sci., 2003, Vol. 13, No. 2, 179 184 QUANTITATIVE L P STABILITY ANALYSIS OF A CLASS OF LINEAR TIME-VARYING FEEDBACK SYSTEMS PINI GURFIL Department of Mechanical and Aerospace
More informationIMC-like Analytical H design with S/SP mixed sensitivity consideration: Utility in PID tuning guidance
IMC-like Analytical H design with S/SP mixed sensitivity consideration: Utility in PID tuning guidance S. Alcántara,a, W. D. Zhang b, C. Pedret a, R. Vilanova a, S. Skogestad c a Department of Telecommunications
More informationOptimal triangular approximation for linear stable multivariable systems
Proceedings of the 007 American Control Conference Marriott Marquis Hotel at Times Square New York City, USA, July -3, 007 Optimal triangular approximation for linear stable multivariable systems Diego
More informationOptimal Polynomial Control for Discrete-Time Systems
1 Optimal Polynomial Control for Discrete-Time Systems Prof Guy Beale Electrical and Computer Engineering Department George Mason University Fairfax, Virginia Correspondence concerning this paper should
More informationChapter 5 The SIMC Method for Smooth PID Controller Tuning
Chapter 5 The SIMC Method for Smooth PID Controller Tuning Sigurd Skogestad and Chriss Grimholt 5.1 Introduction Although the proportional-integral-derivative (PID) controller has only three parameters,
More information1 Loop Control. 1.1 Open-loop. ISS0065 Control Instrumentation
Lecture 4 ISS0065 Control Instrumentation 1 Loop Control System has a continuous signal (analog) basic notions: open-loop control, close-loop control. 1.1 Open-loop Open-loop / avatud süsteem / открытая
More informationROBUST STABILITY AND PERFORMANCE ANALYSIS OF UNSTABLE PROCESS WITH DEAD TIME USING Mu SYNTHESIS
ROBUST STABILITY AND PERFORMANCE ANALYSIS OF UNSTABLE PROCESS WITH DEAD TIME USING Mu SYNTHESIS I. Thirunavukkarasu 1, V. I. George 1, G. Saravana Kumar 1 and A. Ramakalyan 2 1 Department o Instrumentation
More informationSmith Predictor Based Autotuners for Time-delay Systems
Smith Predictor Based Autotuners for Time-dela Sstems ROMAN PROKOP, JIŘÍ KORBEL, RADEK MATUŠŮ Facult of Applied Informatics Tomas Bata Universit in Zlín Nám. TGM 5555, 76 Zlín CZECH REPUBLIC prokop@fai.utb.cz
More informationOn an internal multimodel control for nonlinear multivariable systems - A comparative study
On an internal multimodel control for nonlinear multivariable systems A comparative study Nahla Touati Karmani Dhaou Soudani Mongi Naceur Mohamed Benrejeb Abstract An internal multimodel control designed
More information3.1 Overview 3.2 Process and control-loop interactions
3. Multivariable 3.1 Overview 3.2 and control-loop interactions 3.2.1 Interaction analysis 3.2.2 Closed-loop stability 3.3 Decoupling control 3.3.1 Basic design principle 3.3.2 Complete decoupling 3.3.3
More informationAnalysis of SISO Control Loops
Chapter 5 Analysis of SISO Control Loops Topics to be covered For a given controller and plant connected in feedback we ask and answer the following questions: Is the loop stable? What are the sensitivities
More informationChapter 15 - Solved Problems
Chapter 5 - Solved Problems Solved Problem 5.. Contributed by - Alvaro Liendo, Universidad Tecnica Federico Santa Maria, Consider a plant having a nominal model given by G o (s) = s + 2 The aim of the
More informationPD controller for second order unstable systems with time-delay
Automático, AMCA 215, 43 PD controller for second order unstable systems with time-delay David F. Novella Rodriguez Basilio del Muro Cuéllar Juan Fransisco Márquez Rubio Martin Velasco-Villa Escuela Superior
More informationAdditional Closed-Loop Frequency Response Material (Second edition, Chapter 14)
Appendix J Additional Closed-Loop Frequency Response Material (Second edition, Chapter 4) APPENDIX CONTENTS J. Closed-Loop Behavior J.2 Bode Stability Criterion J.3 Nyquist Stability Criterion J.4 Gain
More informationControl Systems II. ETH, MAVT, IDSC, Lecture 4 17/03/2017. G. Ducard
Control Systems II ETH, MAVT, IDSC, Lecture 4 17/03/2017 Lecture plan: Control Systems II, IDSC, 2017 SISO Control Design 24.02 Lecture 1 Recalls, Introductory case study 03.03 Lecture 2 Cascaded Control
More informationController Design Based on Transient Response Criteria. Chapter 12 1
Controller Design Based on Transient Response Criteria Chapter 12 1 Desirable Controller Features 0. Stable 1. Quik responding 2. Adequate disturbane rejetion 3. Insensitive to model, measurement errors
More informationFurther Results on Model Structure Validation for Closed Loop System Identification
Advances in Wireless Communications and etworks 7; 3(5: 57-66 http://www.sciencepublishinggroup.com/j/awcn doi:.648/j.awcn.735. Further esults on Model Structure Validation for Closed Loop System Identification
More informationChapter 13 Digital Control
Chapter 13 Digital Control Chapter 12 was concerned with building models for systems acting under digital control. We next turn to the question of control itself. Topics to be covered include: why one
More informationDr Ian R. Manchester
Week Content Notes 1 Introduction 2 Frequency Domain Modelling 3 Transient Performance and the s-plane 4 Block Diagrams 5 Feedback System Characteristics Assign 1 Due 6 Root Locus 7 Root Locus 2 Assign
More informationClosed-loop system 2/1/2016. Generally MIMO case. Two-degrees-of-freedom (2 DOF) control structure. (2 DOF structure) The closed loop equations become
Closed-loop system enerally MIMO case Two-degrees-of-freedom (2 DOF) control structure (2 DOF structure) 2 The closed loop equations become solving for z gives where is the closed loop transfer function
More information7.2 Controller tuning from specified characteristic polynomial
192 Finn Haugen: PID Control 7.2 Controller tuning from specified characteristic polynomial 7.2.1 Introduction The subsequent sections explain controller tuning based on specifications of the characteristic
More informationIncorporating Feedforward Action into Self-optimizing Control Policies
Incorporating Feedforward Action into Self-optimizing Control Policies Lia Maisarah Umar, Yi Cao and Vinay Kariwala School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore
More informationAssessing the Robust Stability and Robust Performance by Classical Statistical Concepts Nilton Silva*, Heleno Bispo, João Manzi
A publication of 1393 CHEMICAL ENGINEERING TRANSACTIONS VOL. 3, 013 Chief Editors: Sauro Pierucci, Jiří J. Klemeš Copyright 013, AIDIC Servizi S.r.l., ISBN 978-88-95608-3-5; ISSN 1974-9791 The Italian
More informationDynamic Matrix controller based on Sliding Mode Control.
AMERICAN CONFERENCE ON APPLIED MATHEMATICS (MATH '08, Harvard, Massachusetts, USA, March -, 008 Dynamic Matrix controller based on Sliding Mode Control. OSCAR CAMACHO 1 LUÍS VALVERDE. EDINZO IGLESIAS..
More informationDecoupling Multivariable Control with Two Degrees of Freedom
Article Subscriber access provided by NATIONAL TAIWAN UNIV Decoupling Multivariable Control with Two Degrees of Freedom Hsiao-Ping Huang, and Feng-Yi Lin Ind. Eng. Chem. Res., 2006, 45 (9), 36-373 DOI:
More informationImproved Identification and Control of 2-by-2 MIMO System using Relay Feedback
CEAI, Vol.17, No.4 pp. 23-32, 2015 Printed in Romania Improved Identification and Control of 2-by-2 MIMO System using Relay Feedback D.Kalpana, T.Thyagarajan, R.Thenral Department of Instrumentation Engineering,
More informationUncertainty and Robustness for SISO Systems
Uncertainty and Robustness for SISO Systems ELEC 571L Robust Multivariable Control prepared by: Greg Stewart Outline Nature of uncertainty (models and signals). Physical sources of model uncertainty. Mathematical
More informationMIMO Smith Predictor: Global and Structured Robust Performance Analysis
MIMO Smith Predictor: Global and Structured Robust Performance Analysis Ricardo S. Sánchez-Peña b,, Yolanda Bolea and Vicenç Puig Sistemas Avanzados de Control Universitat Politècnica de Catalunya (UPC)
More informationRobust Internal Model Control for Impulse Elimination of Singular Systems
International Journal of Control Science and Engineering ; (): -7 DOI:.59/j.control.. Robust Internal Model Control for Impulse Elimination of Singular Systems M. M. Share Pasandand *, H. D. Taghirad Department
More informationL 1 Adaptive Output Feedback Controller to Systems of Unknown
Proceedings of the 27 American Control Conference Marriott Marquis Hotel at Times Square New York City, USA, July 11-13, 27 WeB1.1 L 1 Adaptive Output Feedback Controller to Systems of Unknown Dimension
More information